{
  "session_id": "20260521_100143_b58ae9",
  "model": "deepseek-v4-pro",
  "base_url": "https://ollama.com/v1",
  "platform": "telegram",
  "session_start": "2026-05-21T10:01:43.524589",
  "last_updated": "2026-05-21T10:02:44.752081",
  "system_prompt": "You are Hermes Agent, an intelligent AI assistant created by Nous Research. You are helpful, knowledgeable, and direct. You assist users with a wide range of tasks including answering questions, writing and editing code, analyzing information, creative work, and executing actions via your tools. You communicate clearly, admit uncertainty when appropriate, and prioritize being genuinely useful over being verbose unless otherwise directed below. Be targeted and efficient in your exploration and investigations.\n\nIf the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. Docs: https://hermes-agent.nousresearch.com/docs\n\nConversation started: Thursday, May 21, 2026 10:01 AM\nModel: deepseek-v4-pro\nProvider: custom\n\nYou are on a text messaging communication platform, Telegram. Standard markdown is automatically converted to Telegram format. Supported: **bold**, *italic*, ~~strikethrough~~, ||spoiler||, `inline code`, ```code blocks```, [links](url), and ## headers. Telegram has NO table syntax — prefer bullet lists or labeled key: value pairs over pipe tables (any tables you do emit are auto-rewritten into row-group bullets, which you can produce directly for cleaner output). You can send media files natively: to deliver a file to the user, include MEDIA:/absolute/path/to/file in your response. Images (.png, .jpg, .webp) appear as photos, audio (.ogg) sends as voice bubbles, and videos (.mp4) play inline. You can also include image URLs in markdown format ![alt](url) and they will be sent as native photos.",
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "process",
        "description": "Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts), 'close' (close stdin/send EOF).",
        "parameters": {
          "type": "object",
          "properties": {
            "action": {
              "type": "string",
              "enum": [
                "list",
                "poll",
                "log",
                "wait",
                "kill",
                "write",
                "submit",
                "close"
              ],
              "description": "Action to perform on background processes"
            },
            "session_id": {
              "type": "string",
              "description": "Process session ID (from terminal background output). Required for all actions except 'list'."
            },
            "data": {
              "type": "string",
              "description": "Text to send to process stdin (for 'write' and 'submit' actions)"
            },
            "timeout": {
              "type": "integer",
              "description": "Max seconds to block for 'wait' action. Returns partial output on timeout.",
              "minimum": 1
            },
            "offset": {
              "type": "integer",
              "description": "Line offset for 'log' action (default: last 200 lines)"
            },
            "limit": {
              "type": "integer",
              "description": "Max lines to return for 'log' action",
              "minimum": 1
            }
          },
          "required": [
            "action"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "terminal",
        "description": "Execute shell commands on a Linux environment. Filesystem usually persists between calls.\n\nDo NOT use cat/head/tail to read files — use read_file instead.\nDo NOT use grep/rg/find to search — use search_files instead.\nDo NOT use ls to list directories — use search_files(target='files') instead.\nDo NOT use sed/awk to edit files — use patch instead.\nDo NOT use echo/cat heredoc to create files — use write_file instead.\nReserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.\n\nForeground (default): Commands return INSTANTLY when done, even if the timeout is high. Set timeout=300 for long builds/scripts — you'll still get the result in seconds if it's fast. Prefer foreground for short commands.\nBackground: Set background=true to get a session_id. Two patterns:\n  (1) Long-lived processes that never exit (servers, watchers).\n  (2) Long-running tasks with notify_on_complete=true — you can keep working on other things and the system auto-notifies you when the task finishes. Great for test suites, builds, deployments, or anything that takes more than a minute.\nFor servers/watchers, do NOT use shell-level background wrappers (nohup/disown/setsid/trailing '&') in foreground mode. Use background=true so Hermes can track lifecycle and output.\nAfter starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.\nUse process(action=\"poll\") for progress checks, process(action=\"wait\") to block until done.\nWorking directory: Use 'workdir' for per-command cwd.\nPTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).\n\nDo NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.\n",
        "parameters": {
          "type": "object",
          "properties": {
            "command": {
              "type": "string",
              "description": "The command to execute on the VM"
            },
            "background": {
              "type": "boolean",
              "description": "Run the command in the background. Two patterns: (1) Long-lived processes that never exit (servers, watchers). (2) Long-running tasks paired with notify_on_complete=true — you can keep working and get notified when the task finishes. For short commands, prefer foreground with a generous timeout instead.",
              "default": false
            },
            "timeout": {
              "type": "integer",
              "description": "Max seconds to wait (default: 180, foreground max: 600). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily. Foreground timeout above 600s is rejected; use background=true for longer commands.",
              "minimum": 1
            },
            "workdir": {
              "type": "string",
              "description": "Working directory for this command (absolute path). Defaults to the session working directory."
            },
            "pty": {
              "type": "boolean",
              "description": "Run in pseudo-terminal (PTY) mode for interactive CLI tools like Codex, Claude Code, or Python REPL. Only works with local and SSH backends. Default: false.",
              "default": false
            },
            "notify_on_complete": {
              "type": "boolean",
              "description": "When true (and background=true), you'll be automatically notified exactly once when the process finishes. **This is the right choice for almost every long-running task** — tests, builds, deployments, multi-item batch jobs, anything that takes over a minute and has a defined end. Use this and keep working on other things; the system notifies you on exit. MUTUALLY EXCLUSIVE with watch_patterns — when both are set, watch_patterns is dropped.",
              "default": false
            },
            "watch_patterns": {
              "type": "array",
              "items": {
                "type": "string"
              },
              "description": "Strings to watch for in background process output. HARD RATE LIMIT: at most 1 notification per 15 seconds per process — matches arriving inside the cooldown are dropped. After 3 consecutive 15-second windows with dropped matches, watch_patterns is automatically disabled for that process and promoted to notify_on_complete behavior (one notification on exit, no more mid-process spam). USE ONLY for truly rare, one-shot mid-process signals on LONG-LIVED processes that will never exit on their own — e.g. ['Application startup complete'] on a server so you know when to hit its endpoint, or ['migration done'] on a daemon. DO NOT use for: (1) end-of-run markers like 'DONE'/'PASS' — use notify_on_complete instead; (2) error patterns like 'ERROR'/'Traceback' in loops or multi-item batch jobs — they fire on every iteration and you'll hit the strike limit fast; (3) anything you'd ever combine with notify_on_complete. When in doubt, choose notify_on_complete. MUTUALLY EXCLUSIVE with notify_on_complete — set one, not both."
            }
          },
          "required": [
            "command"
          ]
        }
      }
    }
  ],
  "message_count": 22,
  "messages": [
    {
      "role": "user",
      "content": "Research Honcho self-hosting setup from the plastic-labs/honcho GitHub repository. Find: (1) Docker Compose file or docker run command for self-hosting, (2) required environment variables, (3) default port, (4) any Postgres dependency, (5) the base URL format needed after setup. Return all details needed to self-host Honcho on a Linux server with Docker."
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "The user wants me to research the Honcho self-hosting setup from the plastic-labs/honcho GitHub repository. Let me clone the repo and explore it to find all the relevant information.",
      "finish_reason": "tool_calls",
      "reasoning_content": "The user wants me to research the Honcho self-hosting setup from the plastic-labs/honcho GitHub repository. Let me clone the repo and explore it to find all the relevant information.",
      "tool_calls": [
        {
          "id": "call_y2cya652",
          "call_id": "call_y2cya652",
          "response_item_id": "fc_y2cya652",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"cd /DATA \\u0026\\u0026 git clone --depth 1 https://github.com/plastic-labs/honcho.git 2\\u003e\\u00261\",\"timeout\":30}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"fatal: could not create work tree dir 'honcho': Permission denied\", \"exit_code\": 128, \"error\": null}",
      "tool_call_id": "call_y2cya652"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "Can't clone into /DATA directly. Let me check the workspace and try a different location.",
      "finish_reason": "tool_calls",
      "reasoning_content": "Can't clone into /DATA directly. Let me check the workspace and try a different location.",
      "tool_calls": [
        {
          "id": "call_mhdyb0e7",
          "call_id": "call_mhdyb0e7",
          "response_item_id": "fc_mhdyb0e7",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"ls -la /DATA \\u0026\\u0026 whoami \\u0026\\u0026 mkdir -p /tmp/honcho-research 2\\u003e\\u00261\",\"timeout\":10}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"total 2672452\\ndrwxr-xr-x 25 root root        4096 May 18 10:44 .\\ndrwxr-xr-x 19 root root         310 Apr 21 14:30 ..\\ndrwxrwxrwx 14 root root        4096 May 16 10:57 AppData\\ndrwxrwxrwx  2 root root        4096 Apr 13 01:24 Backup\\n-rw-r--r--  1 root root          65 Apr 13 01:24 .bash_profile\\n-rw-r--r--  1 root root        6635 Apr 13 01:24 .bashrc\\ndrwxr-xr-x  6 root root        4096 May 18 10:44 bin\\ndrwx------  3 root root        4096 Apr 13 01:24 .bluetooth\\ndrwxr-xr-x  3 az-a samba       4096 May 11 09:45 .cache\\ndrwxr-xr-x  9 root root        4096 May 21 09:50 .casaos\\ndrwx--x--- 13 root root        4096 May 17 22:02 .docker\\ndrwxrwxrwx  2 root root        4096 Apr 13 01:24 Documents\\ndrwxrwxrwx  2 root root        4096 Apr 15 14:02 Downloads\\n-rwxrw-rw-  1 root root        8196 May  8 20:41 .DS_Store\\ndrwxr-xr-x  2 root root        4096 May 18 13:45 .extensions\\ndrwxrwxrwx  2 root root        4096 Apr 15 14:25 Gallery\\ndrwx------ 13 az-a samba       4096 May 21 10:01 .hermes\\ndrwxr-xr-x  3 root root        4096 Apr 13 01:24 .icewhale\\ndrwxr-xr-x 13 root root        4096 May 17 22:02 .libvirt\\ndrwxr-xr-x 12 az-a samba       4096 May 20 18:53 .local\\ndrwxr-xr-x  8 root root        4096 Apr 13 01:24 .log\\ndrwx------  2 root root       16384 Apr 13 01:23 lost+found\\ndrwxr-xr-x  4 root root        4096 May  8 13:49 .media\\ndrwxr-xr-x  8 root root        4096 Apr 23 15:47 Media\\n-rw-------  1 root root       18725 May  2 18:19 openclaw-backup.json\\ndrwxr-xr-x  4 root root        4096 Apr 13 12:43 .opt\\ndrwxr-xr-x  4 root root        4096 Apr 21 16:00 .ota\\ndrwx------  2 root root        4096 May  2 09:37 .ssh\\n-rw-------  1 root root  2736415744 Apr 28 10:18 .swapfile\\ndrwxr-xr-x 10 root root        4096 May 12 16:39 .trash\\n-rw-r--r--  1 root root        3189 Apr 20 10:19 udo systemctl status casaos-app-management\\n-rw-r--r--  1 root root        1662 Apr 13 01:24 .wgetrc\\naz-a\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_mhdyb0e7"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "The /DATA directory is owned by root, but I'm running as az-a. Let me clone to a writable location.",
      "finish_reason": "tool_calls",
      "reasoning_content": "The /DATA directory is owned by root, but I'm running as az-a. Let me clone to a writable location.",
      "tool_calls": [
        {
          "id": "call_a1v7ys8w",
          "call_id": "call_a1v7ys8w",
          "response_item_id": "fc_a1v7ys8w",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"cd /tmp \\u0026\\u0026 rm -rf honcho \\u0026\\u0026 git clone --depth 1 https://github.com/plastic-labs/honcho.git 2\\u003e\\u00261\",\"timeout\":30}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"Cloning into 'honcho'...\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_a1v7ys8w"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "The clone seems to hang. Let me wait for it. Actually, the output just says \"Cloning into 'honcho'...\" and the command exited successfully. Let me check what's there.",
      "finish_reason": "tool_calls",
      "reasoning_content": "The clone seems to hang. Let me wait for it. Actually, the output just says \"Cloning into 'honcho'...\" and the command exited successfully. Let me check what's there.",
      "tool_calls": [
        {
          "id": "call_j4mn34qa",
          "call_id": "call_j4mn34qa",
          "response_item_id": "fc_j4mn34qa",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"ls -la /tmp/honcho/ 2\\u003e\\u00261\",\"timeout\":10}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"total 1044\\ndrwxr-xr-x 18 az-a samba    720 May 21 10:01 .\\ndrwxrwxrwt 22 root root     880 May 21 10:01 ..\\n-rw-r--r--  1 az-a samba   3722 May 21 10:01 alembic.ini\\ndrwxr-xr-x  2 az-a samba     60 May 21 10:01 assets\\n-rw-r--r--  1 az-a samba  28523 May 21 10:01 CHANGELOG.md\\ndrwxr-xr-x  3 az-a samba     60 May 21 10:01 .claude\\n-rw-r--r--  1 az-a samba  19099 May 21 10:01 CLAUDE.md\\n-rw-r--r--  1 az-a samba   6960 May 21 10:01 config.toml.example\\n-rw-r--r--  1 az-a samba   6577 May 21 10:01 CONTRIBUTING.md\\ndrwxr-xr-x  2 az-a samba     60 May 21 10:01 database\\ndrwxr-xr-x  2 az-a samba    100 May 21 10:01 docker\\n-rw-r--r--  1 az-a samba   3987 May 21 10:01 docker-compose.yml.example\\n-rw-r--r--  1 az-a samba   1725 May 21 10:01 Dockerfile\\n-rw-r--r--  1 az-a samba    146 May 21 10:01 .dockerignore\\ndrwxr-xr-x  9 az-a samba    280 May 21 10:01 docs\\n-rw-r--r--  1 az-a samba  12671 May 21 10:01 .env.template\\ndrwxr-xr-x  8 az-a samba    160 May 21 10:01 examples\\n-rw-r--r--  1 az-a samba    740 May 21 10:01 fly.toml\\ndrwxr-xr-x  7 az-a samba    260 May 21 10:01 .git\\ndrwxr-xr-x  5 az-a samba    100 May 21 10:01 .github\\n-rw-r--r--  1 az-a samba   3377 May 21 10:01 .gitignore\\ndrwxr-xr-x  5 az-a samba    160 May 21 10:01 honcho-cli\\n-rw-r--r--  1 az-a samba  34523 May 21 10:01 LICENSE\\n-rw-r--r--  1 az-a samba    290 May 21 10:01 .markdownlint.json\\ndrwxr-xr-x  3 az-a samba    220 May 21 10:01 mcp\\ndrwxr-xr-x  3 az-a samba    140 May 21 10:01 migrations\\n-rw-r--r--  1 az-a samba   4377 May 21 10:01 .pre-commit-config.yaml\\n-rw-r--r--  1 az-a samba   3654 May 21 10:01 pyproject.toml\\n-rw-r--r--  1 az-a samba      5 May 21 10:01 .python-version\\n-rw-r--r--  1 az-a samba  26715 May 21 10:01 README.md\\ndrwxr-xr-x  2 az-a samba    280 May 21 10:01 scripts\\ndrwxr-xr-x  4 az-a samba     80 May 21 10:01 sdks\\ndrwxr-xr-x 16 az-a samba    520 May 21 10:01 src\\ndrwxr-xr-x 22 az-a samba    620 May 21 10:01 tests\\n-rw-r--r--  1 az-a samba 875553 May 21 10:01 uv.lock\\ndrwxr-xr-x  2 az-a samba    100 May 21 10:01 .vscode\", \"exit_code\": 0, \"error\": null}\n\n[Subdirectory context discovered: /tmp/honcho/CLAUDE.md]\n# CLAUDE.md\n\nThis file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.\n\n# Honcho Overview\n\n## What is Honcho?\n\nHoncho is an infrastructure layer for building AI agents with memory and social cognition. Its primary purposes include:\n\n- Imbuing agents with a sense of identity\n- Personalizing user experiences through understanding user psychology\n- Providing a Chat Endpoint (the Dialectic agent) that injects personal context just-in-time\n- Supporting development of LLM-powered applications that adapt to end users\n- Enabling multi-peer sessions where multiple participants (users or agents) can interact\n\nHoncho leverages the inherent reasoning capabilities of LLMs to build coherent models of user psychology over time, enabling more personalized and effective AI interactions.\n\n## Core Concepts\n\n### Peer Paradigm\n\nHoncho uses a peer-based model where both users and agents are represented as \"peers\". This unified approach enables:\n\n- Multi-participant sessions with mixed human and AI agents\n- Configurable observation settings (which peers observe which others)\n- Flexible identity management for all participants\n\n### Key Primitives\n\n- **Workspace** (formerly App): The root organizational unit containing all resources\n- **Peer** (formerly User): Any participant in the system (human or AI)\n- **Session**: A conversation context that can involve multiple peers\n- **Message**: Data units that can represent communication between peers OR arbitrary data ingested by a peer to enhance its global representation\n- **Collections & Documents**: Internal vector storage for peer representations. Collections are keyed by `(observer, observed)` peer pairs. Collections/Documents are not directly exposed via API, but the observations stored within them are exposed as **Conclusions** (see `/v3/.../conclusions` endpoints).\n\n## Architecture Overview\n\n### API Structure\n\nAll API routes follow the pattern: `/v3/{resource}/{id}/{action}`. Most \"list/search\" endpoints are `POST` so they can accept rich filter bodies.\n\n- **Workspaces**: Create, list, update, search\n- **Peers**: Create, list, update, chat (dialectic), messages, representation\n- **Sessions**: Create, list, update, delete, clone, manage peers, get context\n- **Messages**: Create (batch up to 100), upload (file), list, get, update\n- **Conclusions**: Create, list, query (semantic search), delete — the API-facing name for observations stored in `(observer, observed)` collections\n- **Keys**: Create scoped JWTs\n- **Webhooks**: Register endpoint, list, delete, test\n\n### Key Features\n\n#### Chat Endpoint (Dialectic agent) (`/peers/{peer_id}/chat`)\n\n- Provides bespoke responses informed by the representation\n- Integrates long-term facts from vector storage\n- Supports streaming responses\n- Configurable LLM providers\n\n#### Message Processing Pipeline\n\n1. Messages created via API (batch or single)\n2. Enqueued for background processing:\n   - `representation`: Update peer's context\n   - `summary`: Create session summaries\n3. Session-based queue processing ensures order\n4. Results stored internally in vector DB\n\n### Configuration\n\n- Hierarchical config: config.toml + environment variables\n- Database settings with connection pooling\n- Multiple LLM provider support\n- Background worker (deriver) settings\n- Authentication can be toggled on/off\n\n## Development Guide\n\n### Commands\n\n- Setup: `uv sync`\n- Run server: `uv run fastapi dev src/main.py`\n- Run tests: `uv run pytest tests/`\n- Run single test: `uv run pytest tests/path/to/test_file.py::test_function`\n- Linting: `uv run ruff check src/`\n- Typechecking: `uv run basedpyright`\n- Format code: `uv run ruff format src/`\n\n### SDK Testing\n\n#### TypeScript SDK\n\n**🚨 DO NOT RUN `bun test` DIRECTLY. IT WILL NOT WORK. 🚨**\n\nThe TypeScript SDK tests require a running Honcho server with database and Redis. Running `bun test` alone will fail immediately because there's no server. The tests are orchestrated via pytest which handles all the infrastructure setup.\n\n**The ONLY way to run TypeScript SDK tests:**\n\n```bash\n# From the monorepo root (not from sdks/typescript/)\nuv run pytest tests/ -k typescript\n```\n\n**To type-check the TypeScript SDK (this is fine to run directly):**\n\n```bash\ncd sdks/typescript && bun run tsc --noEmit\n```\n\n### Code Style\n\n- Follow isort conventions with absolute imports preferred\n- Use explicit type hints with SQLAlchemy mapped_column annotations\n- snake_case for variables/functions; PascalCase for classes\n- Line length: 88 chars (Black compatible)\n- Explicit error handling with appropriate exception types\n- Docstrings: Use Google style docstrings\n- **Never hold a DB session during external calls** (LLM, embedding, HTTP). If a function needs both a DB session and an external call result, compute the external result first and pass it as a parameter. This avoids tying up DB connections during slow network I/O. Use `tracked_db` for short-lived, DB-only operations; pass a shared session when multiple DB-only calls can reuse one connection.\n\n### Runtime Architecture\n\nHoncho runs as two cooperating processes that share a Postgres database and Redis cache:\n\n- **API server** (`uv run fastapi dev src/main.py`) — handles HTTP, enqueues background work, returns immediately. Hosts the **Dialectic** agent inline (synchronous tool loop during chat requests).\n- **Deriver worker** (`uv run python -m src.deriver`) — long-running queue consumer (uvloop). Runs the **Deriver**, **Summarizer**, and **Dreamer** off the queue. Can run multiple instances (`DERIVER_WORKERS`). Also hosts an in-process **Reconciler scheduler** (`src/reconciler/`) that periodically embeds messages with `sync_state='pending'` in `MessageEmbedding` and cleans up stale queue items — embedding generation is decoupled from message creation by design.\n\n### Agent Architecture\n\nHoncho uses several specialized LLM agents. They share tool definitions and the LLM client abstraction in `src/utils/agent_tools.py` + `src/llm/`.\n\n> **Terminology:** what users see as **conclusions** (the public API surface and the term we use in documentation) is called **observations** in code symbols — `create_observations`, `delete_observations`, `get_observation_context`, etc. Doc prose below uses \"conclusions\"; references to actual code symbols stay as \"observations.\"\n\n#### 1. Deriver (`src/deriver/`)\n\n**Role**: Memory formation through content ingestion.\n\nThe Deriver processes batches of incoming messages and extracts conclusions about peers. The current architecture is \"minimal deriver\" — a **single LLM call** per batch using structured output, not an agentic tool loop. This trades flexibility for cost and predictability.\n\n- **Trigger**: Messages enqueued by `src/deriver/enqueue.py` on message create; consumed by `src/deriver/queue_manager.py` → `consumer.process_item()` → `deriver.process_representation_tasks_batch()`.\n- **Output**: Explicit conclusions (direct facts) and deductive conclusions (inferences) saved to `(observer, observed)` collections.\n- **Entry point**: `src/deriver/__main__.py` → `queue_manager.main()`.\n- **Prompts**: `src/deriver/prompts.py` (`minimal_deriver_prompt`).\n\n#### 2. Dialectic (`src/dialectic/`)\n\n**Role**: Analysis and recall for answering queries.\n\nThe Dialectic answers questions about peers by strategically gathering context from memory. It is the only tool-using agent on the synchronous request path — it loops over `DIALECTIC_TOOLS` until it has enough context to answer. (The Dreamer specialists also use tools, but run off the queue.)\n\n- **Trigger**: API call to `POST /v3/.../peers/{peer_id}/chat`.\n- **Tools** (see `DIALECTIC_TOOLS` in `src/utils/agent_tools.py`): `search_memory`, `search_messages`, `get_observation_context`, `grep_messages`, `get_messages_by_date_range`, `search_messages_temporal`, `get_reasoning_chain`. At the `minimal` reasoning level, a reduced set (`DIALECTIC_TOOLS_MINIMAL`) is used: just `search_memory` + `search_messages`.\n- **Reasoning levels**: 5 t\n\n[...truncated CLAUDE.md: 18,540 chars total]",
      "tool_call_id": "call_j4mn34qa"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "Let me now read the key files for self-hosting setup: docker-compose.yml.example, .env.template, Dockerfile, and the docker directory.",
      "finish_reason": "tool_calls",
      "reasoning_content": "Let me now read the key files for self-hosting setup: docker-compose.yml.example, .env.template, Dockerfile, and the docker directory.",
      "tool_calls": [
        {
          "id": "call_qb4dsfou",
          "call_id": "call_qb4dsfou",
          "response_item_id": "fc_qb4dsfou",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"cat /tmp/honcho/docker-compose.yml.example\",\"timeout\":10}"
          }
        },
        {
          "id": "call_fgo1k7a4",
          "call_id": "call_fgo1k7a4",
          "response_item_id": "fc_fgo1k7a4",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"cat /tmp/honcho/.env.template\",\"timeout\":10}"
          }
        },
        {
          "id": "call_trrv9crg",
          "call_id": "call_trrv9crg",
          "response_item_id": "fc_trrv9crg",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"cat /tmp/honcho/Dockerfile\",\"timeout\":10}"
          }
        },
        {
          "id": "call_g9ggv677",
          "call_id": "call_g9ggv677",
          "response_item_id": "fc_g9ggv677",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"ls -la /tmp/honcho/docker/\",\"timeout\":10}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"# Honcho Docker Compose\\n#\\n# Usage:\\n#   cp docker-compose.yml.example docker-compose.yml\\n#   cp .env.template .env  # edit with your provider config\\n#   docker compose up -d --build\\n#\\n# By default, ports are bound to 127.0.0.1 (localhost only).\\n# For development, uncomment the source mounts and monitoring services below.\\n\\nservices:\\n  api:\\n    build:\\n      context: .\\n      dockerfile: Dockerfile\\n    entrypoint: [\\\"sh\\\", \\\"docker/entrypoint.sh\\\"]\\n    depends_on:\\n      database:\\n        condition: service_healthy\\n      redis:\\n        condition: service_healthy\\n    ports:\\n      - \\\"127.0.0.1:8000:8000\\\"\\n    healthcheck:\\n      test:\\n        [\\n          \\\"CMD\\\",\\n          \\\"/app/.venv/bin/python\\\",\\n          \\\"-c\\\",\\n          \\\"import urllib.request; urllib.request.urlopen('http://localhost:8000/health', timeout=2).read()\\\",\\n        ]\\n      interval: 5s\\n      timeout: 5s\\n      retries: 5\\n      start_period: 10s\\n    # -- Development: mount source for live reload --\\n    # volumes:\\n    #   - .:/app\\n    #   - venv:/app/.venv\\n    environment:\\n      - DB_CONNECTION_URI=postgresql+psycopg://postgres:postgres@database:5432/postgres\\n      - CACHE_URL=redis://redis:6379/0?suppress=true\\n      - CACHE_ENABLED=true\\n    env_file:\\n      - path: .env\\n        required: false\\n    restart: unless-stopped\\n\\n  deriver:\\n    build:\\n      context: .\\n      dockerfile: Dockerfile\\n    entrypoint: [\\\"/app/.venv/bin/python\\\", \\\"-m\\\", \\\"src.deriver\\\"]\\n    depends_on:\\n      api:\\n        condition: service_healthy\\n      database:\\n        condition: service_healthy\\n      redis:\\n        condition: service_healthy\\n    # -- Development: mount source for live reload --\\n    # volumes:\\n    #   - .:/app\\n    #   - venv:/app/.venv\\n    environment:\\n      - DB_CONNECTION_URI=postgresql+psycopg://postgres:postgres@database:5432/postgres\\n      - CACHE_URL=redis://redis:6379/0?suppress=true\\n      - CACHE_ENABLED=true\\n    env_file:\\n      - path: .env\\n        required: false\\n    restart: unless-stopped\\n\\n  database:\\n    image: pgvector/pgvector:pg15\\n    restart: unless-stopped\\n    ports:\\n      - \\\"127.0.0.1:5432:5432\\\"\\n    command: [\\\"postgres\\\", \\\"-c\\\", \\\"max_connections=200\\\"]\\n    environment:\\n      - POSTGRES_DB=postgres\\n      - POSTGRES_USER=postgres\\n      - POSTGRES_PASSWORD=postgres\\n      # Allow passwordless connections from the host (port is bound to 127.0.0.1).\\n      # Lets the local test suite and ad-hoc tools connect without supplying a\\n      # password. Do NOT use this in production.\\n      - POSTGRES_HOST_AUTH_METHOD=trust\\n      - PGDATA=/var/lib/postgresql/data/pgdata\\n    volumes:\\n      - ./database/init.sql:/docker-entrypoint-initdb.d/init.sql\\n      - pgdata:/var/lib/postgresql/data/\\n    healthcheck:\\n      test: [\\\"CMD-SHELL\\\", \\\"pg_isready -U postgres -d postgres\\\"]\\n      interval: 5s\\n      timeout: 5s\\n      retries: 5\\n\\n  redis:\\n    image: redis:8.2\\n    restart: unless-stopped\\n    ports:\\n      - \\\"127.0.0.1:6379:6379\\\"\\n    volumes:\\n      - redis-data:/data\\n    healthcheck:\\n      test: [\\\"CMD-SHELL\\\", \\\"redis-cli ping\\\"]\\n      interval: 5s\\n      timeout: 5s\\n      retries: 5\\n\\n  # -- Development: monitoring stack (uncomment to enable) --\\n  # prometheus:\\n  #   image: prom/prometheus:v3.2.1\\n  #   ports:\\n  #     - \\\"127.0.0.1:9090:9090\\\"\\n  #   volumes:\\n  #     - ./docker/prometheus.yml:/etc/prometheus/prometheus.yml:ro\\n  #     - prometheus-data:/prometheus\\n  #   depends_on:\\n  #     api:\\n  #       condition: service_started\\n  # grafana:\\n  #   image: grafana/grafana:11.4.0\\n  #   ports:\\n  #     - \\\"127.0.0.1:3000:3000\\\"\\n  #   environment:\\n  #     - GF_SECURITY_ADMIN_USER=admin\\n  #     - GF_SECURITY_ADMIN_PASSWORD=admin\\n  #     - GF_AUTH_ANONYMOUS_ENABLED=true\\n  #     - GF_AUTH_ANONYMOUS_ORG_ROLE=Viewer\\n  #   volumes:\\n  #     - ./docker/grafana-datasource.yml:/etc/grafana/provisioning/datasources/datasource.yml:ro\\n  #   depends_on:\\n  #     prometheus:\\n  #       condition: service_started\\n\\nvolumes:\\n  pgdata:\\n  redis-data:\\n  # -- Development: uncomment if using source mounts --\\n  # venv:\\n  # prometheus-data:\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_qb4dsfou"
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"# Honcho Environment Variables Template\\n# Copy this file to .env and fill in the appropriate values\\n#\\n# Required variables are marked with (REQUIRED)\\n# Optional variables have default values and can be left commented out\\n\\n# =============================================================================\\n# Application Settings\\n# =============================================================================\\nLOG_LEVEL=INFO\\n# SESSION_OBSERVERS_LIMIT=10\\n# GET_CONTEXT_MAX_TOKENS=100000\\n# MAX_FILE_SIZE=5242880 # Bytes\\n# MAX_MESSAGE_SIZE=25000 # Characters\\n\\n# Embedding settings\\n# EMBED_MESSAGES=true\\n# EMBEDDING_VECTOR_DIMENSIONS=1536\\n# EMBEDDING_MAX_INPUT_TOKENS=8192\\n# EMBEDDING_MAX_TOKENS_PER_REQUEST=300000\\n# EMBEDDING_MODEL_CONFIG__TRANSPORT=openai\\n# EMBEDDING_MODEL_CONFIG__MODEL=text-embedding-3-small\\n# EMBEDDING_MODEL_CONFIG__OVERRIDES__BASE_URL=\\n# EMBEDDING_MODEL_CONFIG__OVERRIDES__API_KEY_ENV=\\n\\n# LANGFUSE_HOST=\\n# LANGFUSE_PUBLIC_KEY=\\n\\n# COLLECT_METRICS_LOCAL=false\\n# LOCAL_METRICS_FILE=metrics.jsonl\\n# REASONING_TRACES_FILE=traces.jsonl  # Path to JSONL file for reasoning traces\\n\\n# NAMESPACE=\\\"honcho\\\"\\n\\n# =============================================================================\\n# Database Settings (REQUIRED)\\n# =============================================================================\\n# Connection URI for PostgreSQL database with pgvector support\\n# Must use postgresql+psycopg prefix for SQLAlchemy compatibility\\nDB_CONNECTION_URI=postgresql+psycopg://postgres:postgres@localhost:5432/postgres\\n\\n# Optional database settings\\n# DB_SCHEMA=public\\n# DB_POOL_CLASS=default\\n# DB_POOL_SIZE=10\\n# DB_MAX_OVERFLOW=20\\n# DB_POOL_TIMEOUT=30\\n# DB_POOL_RECYCLE=300\\n# DB_POOL_PRE_PING=true\\n# DB_POOL_USE_LIFO=true\\n# DB_SQL_DEBUG=false\\n# DB_TRACING=false\\n\\n# =============================================================================\\n# Authentication Settings\\n# =============================================================================\\n# Whether to enable authentication (set to true for production)\\nAUTH_USE_AUTH=false\\n\\n# JWT secret key (REQUIRED if AUTH_USE_AUTH=true)\\n# Generate with: python scripts/generate_jwt_secret.py\\n# AUTH_JWT_SECRET=your-secret-key-here\\n\\n# =============================================================================\\n# LLM Provider (REQUIRED)\\n# =============================================================================\\n# Honcho uses LLMs for memory extraction, summarization, dialectic chat, and\\n# dream consolidation. The server will fail to start without a provider configured.\\n#\\n# Quick start: set LLM_OPENAI_API_KEY below to use the built-in defaults.\\n# Text-generation features default to transport = \\\"openai\\\" and\\n# model = \\\"gpt-5.4-mini\\\". Embeddings default to transport = \\\"openai\\\" and\\n# model = \\\"text-embedding-3-small\\\". For OpenAI-compatible proxies\\n# (OpenRouter, Together, Fireworks, vLLM, Ollama, LiteLLM), override\\n# MODEL_CONFIG__MODEL and MODEL_CONFIG__OVERRIDES__BASE_URL on each feature\\n# section you want to route through that endpoint.\\n# Models must support tool calling (function calling).\\n#\\n# Supported transports: openai, anthropic, gemini\\n# Each transport picks up its API key from the corresponding LLM_*_API_KEY.\\n# Base URLs are set per-module via MODEL_CONFIG__OVERRIDES__BASE_URL.\\n#\\nLLM_OPENAI_API_KEY=your-api-key-here\\n# LLM_ANTHROPIC_API_KEY=\\n# LLM_GEMINI_API_KEY=\\n\\n# =============================================================================\\n# LLM Configuration\\n# =============================================================================\\n# Global LLM settings\\n# LLM_DEFAULT_MAX_TOKENS=2500\\n# LLM_MAX_TOOL_OUTPUT_CHARS=10000  # Max chars for tool output (~2500 tokens)\\n# LLM_MAX_MESSAGE_CONTENT_CHARS=2000  # Max chars per message in tool results\\n\\n# =============================================================================\\n# Deriver (Background Worker)\\n# =============================================================================\\n# DERIVER_ENABLED=true\\n# Defaults:\\n# DERIVER_MODEL_CONFIG__TRANSPORT=openai\\n# DERIVER_MODEL_CONFIG__MODEL=gpt-5.4-mini\\n# Optional overrides:\\n# DERIVER_MODEL_CONFIG__MODEL=your-model-here\\n# DERIVER_MODEL_CONFIG__OVERRIDES__BASE_URL=https://openrouter.ai/api/v1\\n# DERIVER_WORKERS=1\\n# DERIVER_POLLING_SLEEP_INTERVAL_SECONDS=1.0\\n# DERIVER_STALE_SESSION_TIMEOUT_MINUTES=5\\n# DERIVER_QUEUE_ERROR_RETENTION_SECONDS=2592000  # 30 days\\n# DERIVER_MODEL_CONFIG__TEMPERATURE=\\n# DERIVER_MODEL_CONFIG__THINKING_EFFORT=minimal\\n# DERIVER_MODEL_CONFIG__THINKING_BUDGET_TOKENS=1024  # Gemini/Anthropic only\\n# DERIVER_DEDUPLICATE=true\\n# DERIVER_MODEL_CONFIG__MAX_OUTPUT_TOKENS=4096\\n# DERIVER_LOG_OBSERVATIONS=false\\n# DERIVER_MAX_INPUT_TOKENS=25000\\n# DERIVER_MAX_CUSTOM_INSTRUCTIONS_TOKENS=2000\\n# DERIVER_WORKING_REPRESENTATION_MAX_OBSERVATIONS=100\\n# DERIVER_REPRESENTATION_BATCH_MAX_TOKENS=1024\\n# DERIVER_FLUSH_ENABLED=false  # Bypass batch token threshold, process work immediately\\n# DERIVER_MODEL_CONFIG__FALLBACK__MODEL=\\n# DERIVER_MODEL_CONFIG__FALLBACK__TRANSPORT=\\n# DERIVER_MODEL_CONFIG__OVERRIDES__BASE_URL=\\n# DERIVER_MODEL_CONFIG__OVERRIDES__API_KEY_ENV=\\n\\n# =============================================================================\\n# Peer Card\\n# =============================================================================\\n# PEER_CARD_ENABLED=true\\n\\n# =============================================================================\\n# Dialectic\\n# =============================================================================\\n# DIALECTIC_MAX_OUTPUT_TOKENS=8192\\n# DIALECTIC_MAX_INPUT_TOKENS=100000\\n# DIALECTIC_HISTORY_TOKEN_LIMIT=8192\\n# DIALECTIC_SESSION_HISTORY_MAX_TOKENS=4096\\n#\\n# Per-level settings (reasoning_level parameter in API)\\n# Each level has its own nested MODEL_CONFIG, tool iterations, and max output tokens.\\n# MAX_OUTPUT_TOKENS is optional per level; if not set, uses global DIALECTIC_MAX_OUTPUT_TOKENS.\\n# Defaults:\\n# DIALECTIC_LEVELS__minimal__MODEL_CONFIG__TRANSPORT=openai\\n# DIALECTIC_LEVELS__minimal__MODEL_CONFIG__MODEL=gpt-5.4-mini\\n# DIALECTIC_LEVELS__minimal__MAX_TOOL_ITERATIONS=1\\n# DIALECTIC_LEVELS__minimal__MAX_OUTPUT_TOKENS=250\\n# DIALECTIC_LEVELS__minimal__TOOL_CHOICE=auto\\n# DIALECTIC_LEVELS__low__MODEL_CONFIG__TRANSPORT=openai\\n# DIALECTIC_LEVELS__low__MODEL_CONFIG__MODEL=gpt-5.4-mini\\n# DIALECTIC_LEVELS__low__MAX_TOOL_ITERATIONS=5\\n# DIALECTIC_LEVELS__low__TOOL_CHOICE=auto\\n# DIALECTIC_LEVELS__medium__MODEL_CONFIG__TRANSPORT=openai\\n# DIALECTIC_LEVELS__medium__MODEL_CONFIG__MODEL=gpt-5.4-mini\\n# DIALECTIC_LEVELS__medium__MAX_TOOL_ITERATIONS=2\\n# DIALECTIC_LEVELS__high__MODEL_CONFIG__TRANSPORT=openai\\n# DIALECTIC_LEVELS__high__MODEL_CONFIG__MODEL=gpt-5.4-mini\\n# DIALECTIC_LEVELS__high__MAX_TOOL_ITERATIONS=4\\n# DIALECTIC_LEVELS__max__MODEL_CONFIG__TRANSPORT=openai\\n# DIALECTIC_LEVELS__max__MODEL_CONFIG__MODEL=gpt-5.4-mini\\n# DIALECTIC_LEVELS__max__MAX_TOOL_ITERATIONS=10\\n# Optional overrides:\\n# DIALECTIC_LEVELS__minimal__MODEL_CONFIG__MODEL=your-model-here\\n# DIALECTIC_LEVELS__low__MODEL_CONFIG__MODEL=your-model-here\\n# DIALECTIC_LEVELS__medium__MODEL_CONFIG__MODEL=your-model-here\\n# DIALECTIC_LEVELS__high__MODEL_CONFIG__MODEL=your-model-here\\n# DIALECTIC_LEVELS__max__MODEL_CONFIG__MODEL=your-model-here\\n# DIALECTIC_LEVELS__max__MODEL_CONFIG__THINKING_EFFORT=medium\\n# DIALECTIC_LEVELS__max__MODEL_CONFIG__THINKING_BUDGET_TOKENS=1024\\n# Optional backup per level (must set both or neither):\\n# DIALECTIC_LEVELS__max__MODEL_CONFIG__FALLBACK__MODEL=gemini-2.5-pro\\n# DIALECTIC_LEVELS__max__MODEL_CONFIG__FALLBACK__TRANSPORT=gemini\\n\\n# =============================================================================\\n# Summary\\n# =============================================================================\\n# SUMMARY_ENABLED=true\\n# Defaults:\\n# SUMMARY_MODEL_CONFIG__TRANSPORT=openai\\n# SUMMARY_MODEL_CONFIG__MODEL=gpt-5.4-mini\\n# Optional overrides:\\n# SUMMARY_MODEL_CONFIG__MODEL=your-model-here\\n# SUMMARY_MODEL_CONFIG__OVERRIDES__BASE_URL=https://openrouter.ai/api/v1\\n# SUMMARY_MODEL_CONFIG__THINKING_EFFORT=minimal\\n# SUMMARY_MODEL_CONFIG__THINKING_BUDGET_TOKENS=1024  # Gemini/Anthropic only\\n# SUMMARY_MESSAGES_PER_SHORT_SUMMARY=20\\n# SUMMARY_MESSAGES_PER_LONG_SUMMARY=60\\n# SUMMARY_MAX_TOKENS_SHORT=1000\\n# SUMMARY_MAX_TOKENS_LONG=4000\\n# SUMMARY_MODEL_CONFIG__FALLBACK__MODEL=\\n\\n# =============================================================================\\n# Dream\\n# =============================================================================\\n# DREAM_ENABLED=true\\n# Defaults:\\n# DREAM_DEDUCTION_MODEL_CONFIG__TRANSPORT=openai\\n# DREAM_DEDUCTION_MODEL_CONFIG__MODEL=gpt-5.4-mini\\n# DREAM_INDUCTION_MODEL_CONFIG__TRANSPORT=openai\\n# DREAM_INDUCTION_MODEL_CONFIG__MODEL=gpt-5.4-mini\\n# Optional overrides:\\n# DREAM_DEDUCTION_MODEL_CONFIG__MODEL=your-model-here\\n# DREAM_DEDUCTION_MODEL_CONFIG__OVERRIDES__BASE_URL=https://openrouter.ai/api/v1\\n# DREAM_INDUCTION_MODEL_CONFIG__MODEL=your-model-here\\n# DREAM_INDUCTION_MODEL_CONFIG__OVERRIDES__BASE_URL=https://openrouter.ai/api/v1\\n# DREAM_DOCUMENT_THRESHOLD=50\\n# DREAM_IDLE_TIMEOUT_MINUTES=60\\n# DREAM_MIN_HOURS_BETWEEN_DREAMS=8\\n# DREAM_ENABLED_TYPES=[\\\"omni\\\"]\\n# DREAM_MAX_TOOL_ITERATIONS=20\\n# DREAM_HISTORY_TOKEN_LIMIT=16384\\n\\n# Surprisal sampling (advanced):\\n# DREAM_SURPRISAL__ENABLED=false\\n# DREAM_SURPRISAL__TREE_TYPE=kdtree\\n# DREAM_SURPRISAL__TREE_K=5\\n# DREAM_SURPRISAL__SAMPLING_STRATEGY=recent\\n# DREAM_SURPRISAL__SAMPLE_SIZE=200\\n# DREAM_SURPRISAL__TOP_PERCENT_SURPRISAL=0.10\\n# DREAM_SURPRISAL__MIN_HIGH_SURPRISAL_FOR_REPLACE=10\\n# DREAM_SURPRISAL__INCLUDE_LEVELS=[\\\"explicit\\\",\\\"deductive\\\"]\\n\\n# =============================================================================\\n# Webhook Settings\\n# =============================================================================\\n# WEBHOOK_SECRET=\\n# WEBHOOK_MAX_WORKSPACE_LIMIT=10\\n\\n# =============================================================================\\n# Monitoring and Observability (Optional)\\n# =============================================================================\\n# Sentry error tracking\\n# SENTRY_ENABLED=false\\n# SENTRY_DSN=your-sentry-dsn-here\\n# SENTRY_RELEASE=your-release-semver\\n# SENTRY_ENVIRONMENT=development\\n# SENTRY_TRACES_SAMPLE_RATE=0.1\\n# SENTRY_PROFILES_SAMPLE_RATE=0.1\\n\\n# =============================================================================\\n# Prometheus Metrics Settings (Pull-based metrics)\\n# =============================================================================\\n# METRICS_ENABLED=false\\n# METRICS_NAMESPACE=honcho  # Inherits from NAMESPACE if not set\\n\\n# =============================================================================\\n# CloudEvents Telemetry Settings (Analytics events)\\n# =============================================================================\\n# TELEMETRY_ENABLED=false\\n# TELEMETRY_ENDPOINT=https://telemetry.honcho.dev/v1/events\\n# TELEMETRY_HEADERS={\\\"Authorization\\\": \\\"Bearer your-token\\\"}  # JSON string for auth headers\\n# TELEMETRY_BATCH_SIZE=100\\n# TELEMETRY_FLUSH_INTERVAL_SECONDS=1.0\\n# TELEMETRY_FLUSH_THRESHOLD=50\\n# TELEMETRY_MAX_RETRIES=3\\n# TELEMETRY_MAX_BUFFER_SIZE=10000\\n# TELEMETRY_NAMESPACE=honcho  # Inherits from NAMESPACE if not set\\n\\n# =============================================================================\\n# Cache\\n# =============================================================================\\n# CACHE_ENABLED=false\\n# CACHE_URL=\\\"redis://localhost:6379/0?suppress=true\\\"\\n# CACHE_NAMESPACE=\\\"honcho\\\"  # Inherits from NAMESPACE if not set\\n# CACHE_DEFAULT_TTL_SECONDS=300\\n# CACHE_DEFAULT_LOCK_TTL_SECONDS=5\\n\\n# =============================================================================\\n# Vector Store Settings\\n# =============================================================================\\n# Vector store type: \\\"pgvector\\\", \\\"turbopuffer\\\", or \\\"lancedb\\\"\\nVECTOR_STORE_TYPE=pgvector\\n\\n# Migration flag: set to true when migration from pgvector is complete\\nVECTOR_STORE_MIGRATED=false\\n\\n# Global namespace prefix for all vector namespaces\\n# Namespaces follow the pattern: {NAMESPACE}.{type}.{hash}\\n# where hash is a base64url-encoded SHA-256 of the workspace/peer names\\n# - Documents: {NAMESPACE}.doc.{hash(workspace, observer, observed)}\\n# - Messages: {NAMESPACE}.msg.{hash(workspace)}\\n# VECTOR_STORE_NAMESPACE=honcho  # Inherits from NAMESPACE if not set\\n\\n# Embedding dimensions are configured via EMBEDDING_VECTOR_DIMENSIONS (see top\\n# of this file). VECTOR_STORE_DIMENSIONS is deprecated and ignored.\\n\\n# Turbopuffer-specific settings (required if TYPE is \\\"turbopuffer\\\")\\n# VECTOR_STORE_TURBOPUFFER_API_KEY=your-turbopuffer-api-key\\n# VECTOR_STORE_TURBOPUFFER_REGION=gcp-us-east4\\n\\n# LanceDB-specific settings (local embedded mode)\\n# VECTOR_STORE_LANCEDB_PATH=./lancedb_data\\n\\n# Reconciliation interval for background sync (default: 5 minutes)\\n# VECTOR_STORE_RECONCILIATION_INTERVAL_SECONDS=300\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_fgo1k7a4"
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"# https://pythonspeed.com/articles/base-image-python-docker-images/\\n# https://testdriven.io/blog/docker-best-practices/\\nFROM python:3.13-slim-bookworm\\n\\nCOPY --from=ghcr.io/astral-sh/uv:0.9.24 /uv /bin/uv\\n\\n# Set Working directory\\nWORKDIR /app\\n\\n# Enable bytecode compilation\\nENV UV_COMPILE_BYTECODE=1\\n\\n# Copy from the cache instead of linking since it's a mounted volume\\nENV UV_LINK_MODE=copy\\n\\n# Python optimizations\\nENV PYTHONDONTWRITEBYTECODE=1\\nENV PYTHONUNBUFFERED=1\\n\\n# Install the project's dependencies using the lockfile and settings\\nRUN --mount=type=cache,target=/root/.cache/uv \\\\\\n    --mount=type=bind,source=uv.lock,target=uv.lock \\\\\\n    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \\\\\\n    uv sync --frozen --no-install-project --no-group dev\\n\\n# Copy only requirements to cache them in docker layer\\nCOPY uv.lock pyproject.toml /app/\\n\\n# Sync the project\\nRUN --mount=type=cache,target=/root/.cache/uv \\\\\\n    uv sync --frozen --no-group dev\\n\\n# Place executables in the environment at the front of the path\\nENV PATH=\\\"/app/.venv/bin:$PATH\\\"\\nENV HOME=/app\\nENV UV_CACHE_DIR=/tmp/uv-cache\\n\\n# Create non-root user and set ownership\\nRUN addgroup --system app && adduser --system --group app && mkdir -p /tmp/uv-cache && chown -R app:app /app /tmp/uv-cache\\n\\nCOPY --chown=app:app src/ /app/src/\\nCOPY --chown=app:app migrations/ /app/migrations/\\nCOPY --chown=app:app scripts/ /app/scripts/\\nCOPY --chown=app:app docker/ /app/docker/\\nCOPY --chown=app:app alembic.ini /app/alembic.ini\\n# Copy config files - this will copy config.toml if it exists, and config.toml.example\\nCOPY --chown=app:app config.toml* /app/\\n\\n# Switch to non-root user\\nUSER app\\n\\nEXPOSE 8000\\n\\nCMD [\\\"fastapi\\\", \\\"run\\\", \\\"--host\\\", \\\"0.0.0.0\\\", \\\"src/main.py\\\"]\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_trrv9crg"
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"total 12\\ndrwxr-xr-x  2 az-a samba 100 May 21 10:01 .\\ndrwxr-xr-x 18 az-a samba 720 May 21 10:01 ..\\n-rwxr-xr-x  1 az-a samba 192 May 21 10:01 entrypoint.sh\\n-rw-r--r--  1 az-a samba 160 May 21 10:01 grafana-datasource.yml\\n-rw-r--r--  1 az-a samba 206 May 21 10:01 prometheus.yml\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_g9ggv677"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "Let me also check the entrypoint script and the init.sql for database setup, as well as look at how the API base URL is structured in the code.",
      "finish_reason": "tool_calls",
      "reasoning_content": "Let me also check the entrypoint script and the init.sql for database setup, as well as look at how the API base URL is structured in the code.",
      "tool_calls": [
        {
          "id": "call_04j7428s",
          "call_id": "call_04j7428s",
          "response_item_id": "fc_04j7428s",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"cat /tmp/honcho/docker/entrypoint.sh\",\"timeout\":10}"
          }
        },
        {
          "id": "call_38lentmc",
          "call_id": "call_38lentmc",
          "response_item_id": "fc_38lentmc",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"cat /tmp/honcho/database/init.sql\",\"timeout\":10}"
          }
        },
        {
          "id": "call_3c8tvrvh",
          "call_id": "call_3c8tvrvh",
          "response_item_id": "fc_3c8tvrvh",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"cat /tmp/honcho/config.toml.example\",\"timeout\":10}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"#!/bin/sh\\nset -e\\n\\necho \\\"Running database migrations...\\\"\\n/app/.venv/bin/python scripts/provision_db.py\\n\\necho \\\"Starting API server...\\\"\\nexec /app/.venv/bin/fastapi run --host 0.0.0.0 src/main.py\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_04j7428s"
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"CREATE EXTENSION IF NOT EXISTS vector;\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_38lentmc"
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"# Honcho Configuration File\\n# This file demonstrates all available configuration options.\\n# Copy this to config.toml and modify as needed.\\n# Environment variables will override these values.\\n\\n# Application-level settings\\n[app]\\nLOG_LEVEL = \\\"INFO\\\"\\nSESSION_OBSERVERS_LIMIT = 10\\nGET_CONTEXT_MAX_TOKENS = 100000\\nMAX_FILE_SIZE = 5242880  # 5MB\\nMAX_MESSAGE_SIZE = 25000 # Characters\\nEMBED_MESSAGES = true\\n# LANGFUSE_HOST = \\\"https://api.langfuse.com\\\"\\n# LANGFUSE_PUBLIC_KEY = \\\"your-public-key-here\\\"\\n# COLLECT_METRICS_LOCAL = false\\n# LOCAL_METRICS_FILE = \\\"metrics.jsonl\\\"\\n# REASONING_TRACES_FILE = \\\"traces.jsonl\\\"  # Path to JSONL file for reasoning traces\\nNAMESPACE = \\\"honcho\\\"\\n\\n# Database settings\\n[db]\\nCONNECTION_URI = \\\"postgresql+psycopg://postgres:postgres@localhost:5432/postgres\\\"\\nSCHEMA = \\\"public\\\"\\nPOOL_CLASS = \\\"default\\\"\\nPOOL_PRE_PING = true\\nPOOL_SIZE = 10\\nMAX_OVERFLOW = 20\\nPOOL_TIMEOUT = 30  # seconds\\nPOOL_RECYCLE = 300  # seconds\\nPOOL_USE_LIFO = true\\nSQL_DEBUG = false\\nTRACING = false\\n\\n# Authentication settings\\n[auth]\\nUSE_AUTH = false\\nJWT_SECRET = \\\"your-secret-key-here\\\"  # Must be set if USE_AUTH is true\\n\\n# Sentry settings\\n[sentry]\\nENABLED = false\\nDSN = \\\"\\\"\\nRELEASE = \\\"\\\"\\nENVIRONMENT = \\\"development\\\"\\nTRACES_SAMPLE_RATE = 0.1\\nPROFILES_SAMPLE_RATE = 0.1\\n\\n# LLM settings\\n[llm]\\nDEFAULT_MAX_TOKENS = 2500\\nMAX_TOOL_OUTPUT_CHARS = 10000  # Max chars for tool output (~2500 tokens)\\nMAX_MESSAGE_CONTENT_CHARS = 2000  # Max chars per message in tool results\\n\\n# API Keys for LLM providers (set the ones you need)\\n# Supported transports: openai, anthropic, gemini\\n# Base URLs are set per-module via model_config.overrides.base_url\\n# Built-in text-generation defaults use openai / gpt-5.4-mini.\\n# Embeddings default to openai / text-embedding-3-small.\\nOPENAI_API_KEY = \\\"your-api-key-here\\\"\\n# ANTHROPIC_API_KEY = \\\"your-api-key\\\"\\n# GEMINI_API_KEY = \\\"your-api-key\\\"\\n\\n# Embedding settings\\n[embedding]\\nVECTOR_DIMENSIONS = 1536\\nMAX_INPUT_TOKENS = 8192\\nMAX_TOKENS_PER_REQUEST = 300000\\n\\n[embedding.model_config]\\ntransport = \\\"openai\\\"\\nmodel = \\\"text-embedding-3-small\\\"\\n\\n# Optional module-level endpoint overrides\\n# [embedding.model_config.overrides]\\n# base_url = \\\"https://embedding-proxy.internal.example/v1\\\"\\n# api_key_env = \\\"EMBEDDING_CUSTOM_API_KEY\\\"\\n\\n# Deriver settings\\n[deriver]\\nENABLED = true\\nWORKERS = 1\\nPOLLING_SLEEP_INTERVAL_SECONDS = 1.0\\nSTALE_SESSION_TIMEOUT_MINUTES = 5\\n# QUEUE_ERROR_RETENTION_SECONDS = 2592000  # 30 days\\nDEDUPLICATE = true\\nLOG_OBSERVATIONS = false\\nMAX_INPUT_TOKENS = 25000\\nMAX_CUSTOM_INSTRUCTIONS_TOKENS = 2000\\nWORKING_REPRESENTATION_MAX_OBSERVATIONS = 100\\nREPRESENTATION_BATCH_MAX_TOKENS = 1024\\nFLUSH_ENABLED = false  # Bypass batch token threshold, process work immediately\\n\\n[deriver.model_config]\\ntransport = \\\"openai\\\"\\nmodel = \\\"gpt-5.4-mini\\\"\\n# temperature = 0.0\\n# thinking_effort = \\\"minimal\\\"\\n# thinking_budget_tokens = 1024\\n# max_output_tokens = 4096\\n\\n# Optional module-level endpoint overrides\\n# transport = \\\"openai\\\"\\n# model = \\\"my-local-model\\\"\\n# [deriver.model_config.overrides]\\n# base_url = \\\"https://llm.internal.example/v1\\\"\\n# api_key_env = \\\"DERIVER_CUSTOM_API_KEY\\\"\\n\\n# Optional fallback model\\n# [deriver.model_config.fallback]\\n# transport = \\\"anthropic\\\"\\n# model = \\\"claude-haiku-4-5\\\"\\n# [deriver.model_config.fallback.overrides]\\n# base_url = \\\"https://llm-backup.internal.example/v1\\\"\\n# api_key_env = \\\"DERIVER_CUSTOM_BACKUP_API_KEY\\\"\\n# [deriver.model_config.overrides.provider_params]\\n# verbosity = \\\"low\\\"\\n\\n# Peer card settings\\n[peer_card]\\nENABLED = true\\n\\n# Dialectic settings\\n[dialectic]\\nMAX_OUTPUT_TOKENS = 8192\\nMAX_INPUT_TOKENS = 100000\\nHISTORY_TOKEN_LIMIT = 8192\\nSESSION_HISTORY_MAX_TOKENS = 4096\\n\\n# Per-level settings for reasoning levels\\n# MAX_OUTPUT_TOKENS is optional per level; if not set, uses global MAX_OUTPUT_TOKENS\\n[dialectic.levels.minimal]\\nMAX_TOOL_ITERATIONS = 1\\nMAX_OUTPUT_TOKENS = 250\\nTOOL_CHOICE = \\\"auto\\\"\\n\\n[dialectic.levels.minimal.model_config]\\ntransport = \\\"openai\\\"\\nmodel = \\\"gpt-5.4-mini\\\"\\n\\n[dialectic.levels.low]\\nMAX_TOOL_ITERATIONS = 5\\nTOOL_CHOICE = \\\"auto\\\"\\n\\n[dialectic.levels.low.model_config]\\ntransport = \\\"openai\\\"\\nmodel = \\\"gpt-5.4-mini\\\"\\n\\n[dialectic.levels.medium]\\nMAX_TOOL_ITERATIONS = 2\\n\\n[dialectic.levels.medium.model_config]\\ntransport = \\\"openai\\\"\\nmodel = \\\"gpt-5.4-mini\\\"\\n\\n[dialectic.levels.high]\\nMAX_TOOL_ITERATIONS = 4\\n\\n[dialectic.levels.high.model_config]\\ntransport = \\\"openai\\\"\\nmodel = \\\"gpt-5.4-mini\\\"\\n\\n[dialectic.levels.max]\\nMAX_TOOL_ITERATIONS = 10\\n\\n[dialectic.levels.max.model_config]\\ntransport = \\\"openai\\\"\\nmodel = \\\"gpt-5.4-mini\\\"\\n\\n# [dialectic.levels.max.model_config.fallback]\\n# transport = \\\"gemini\\\"\\n# model = \\\"gemini-2.5-pro\\\"\\n\\n# Summary settings\\n[summary]\\nENABLED = true\\nMESSAGES_PER_SHORT_SUMMARY = 20\\nMESSAGES_PER_LONG_SUMMARY = 60\\nMAX_TOKENS_SHORT = 1000\\nMAX_TOKENS_LONG = 4000\\n\\n[summary.model_config]\\ntransport = \\\"openai\\\"\\nmodel = \\\"gpt-5.4-mini\\\"\\n# thinking_effort = \\\"minimal\\\"\\n# thinking_budget_tokens = 1024\\n\\n# [summary.model_config.fallback]\\n# transport = \\\"anthropic\\\"\\n# model = \\\"claude-haiku-4-5\\\"\\n\\n# Dream settings\\n[dream]\\nENABLED = true\\nDOCUMENT_THRESHOLD = 50\\nIDLE_TIMEOUT_MINUTES = 60\\nMIN_HOURS_BETWEEN_DREAMS = 8\\nENABLED_TYPES = [\\\"omni\\\"]\\nMAX_TOOL_ITERATIONS = 20\\nHISTORY_TOKEN_LIMIT = 16384\\n\\n[dream.deduction_model_config]\\ntransport = \\\"openai\\\"\\nmodel = \\\"gpt-5.4-mini\\\"\\n\\n[dream.induction_model_config]\\ntransport = \\\"openai\\\"\\nmodel = \\\"gpt-5.4-mini\\\"\\n\\n# Surprisal-based sampling subsystem\\n[dream.surprisal]\\nENABLED = false\\nTREE_TYPE = \\\"kdtree\\\"  # Options: kdtree, balltree, rptree, covertree, lsh, graph, prototype\\nTREE_K = 5  # k for kNN-based trees\\nSAMPLING_STRATEGY = \\\"recent\\\"  # Options: recent, random, all\\nSAMPLE_SIZE = 200\\nTOP_PERCENT_SURPRISAL = 0.10  # Top 10% of observations\\nMIN_HIGH_SURPRISAL_FOR_REPLACE = 10\\nINCLUDE_LEVELS = [\\\"explicit\\\", \\\"deductive\\\"]\\n\\n# Webhook settings\\n[webhook]\\nSECRET = \\\"\\\"\\nMAX_WORKSPACE_LIMIT = 10\\n\\n# Prometheus metrics settings (pull-based metrics)\\n[metrics]\\nENABLED = false\\n# NAMESPACE = \\\"honcho\\\"  # Inherits from app.NAMESPACE if not set\\n\\n# CloudEvents telemetry settings (analytics events)\\n[telemetry]\\nENABLED = false\\n# ENDPOINT = \\\"https://telemetry.honcho.dev/v1/events\\\"\\n# HEADERS = '{\\\"Authorization\\\": \\\"Bearer your-token\\\"}'  # JSON string for auth headers\\nBATCH_SIZE = 100\\nFLUSH_INTERVAL_SECONDS = 1.0\\nFLUSH_THRESHOLD = 50\\nMAX_RETRIES = 3\\nMAX_BUFFER_SIZE = 10000\\n# NAMESPACE = \\\"honcho\\\"  # Inherits from app.NAMESPACE if not set\\n\\n# Cache settings\\n[cache]\\nENABLED = false\\nURL = \\\"redis://localhost:6379/0?suppress=true\\\"\\n# NAMESPACE = \\\"honcho\\\"  # Inherits from app.NAMESPACE if not set\\nDEFAULT_TTL_SECONDS = 300\\nDEFAULT_LOCK_TTL_SECONDS = 5\\n\\n# Vector store settings\\n[vector_store]\\n# Vector store type: \\\"pgvector\\\", \\\"turbopuffer\\\", or \\\"lancedb\\\"\\nTYPE = \\\"pgvector\\\"\\n# Migration flag: set to true when migration from pgvector is complete\\nMIGRATED = false\\nNAMESPACE = \\\"honcho\\\"\\n# DIMENSIONS is deprecated; embedding.vector_dimensions is authoritative.\\n# TURBOPUFFER_API_KEY = \\\"your-turbopuffer-api-key\\\"\\n# TURBOPUFFER_REGION = \\\"us-east-1\\\"\\nLANCEDB_PATH = \\\"./lancedb_data\\\"\\nRECONCILIATION_INTERVAL_SECONDS = 300\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_3c8tvrvh"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "Let me also check for the health endpoint and any additional documentation about self-hosting.",
      "finish_reason": "tool_calls",
      "reasoning_content": "Let me also check for the health endpoint and any additional documentation about self-hosting.",
      "tool_calls": [
        {
          "id": "call_yocl0h1q",
          "call_id": "call_yocl0h1q",
          "response_item_id": "fc_yocl0h1q",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"grep -rn \\\"health\\\" /tmp/honcho/src/main.py | head -5\",\"timeout\":5}"
          }
        },
        {
          "id": "call_6x7eekip",
          "call_id": "call_6x7eekip",
          "response_item_id": "fc_6x7eekip",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"ls /tmp/honcho/docs/\",\"timeout\":5}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"207:@app.get(\\\"/health\\\")\\n208:async def health_check():\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_yocl0h1q"
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"bun.lock\\nchangelog\\ndocs.json\\nfavicon.svg\\nimages\\nlogo\\npackage.json\\nREADME.md\\nsnippets\\nv1\\nv2\\nv3\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_6x7eekip"
    },
    {
      "role": "assistant",
      "content": "Here is a complete summary of the Honcho self-hosting setup from the `plastic-labs/honcho` repository.\n\n---\n\n## Honcho Self-Hosting Summary\n\n### 1. Docker Compose File\n\nFound at `/tmp/honcho/docker-compose.yml.example`. The setup has **5 services**:\n\n| Service | Role | Image |\n|---------|------|-------|\n| **api** | FastAPI HTTP server (main app) | Built from Dockerfile |\n| **deriver** | Background worker (memory extraction) | Built from Dockerfile |\n| **database** | PostgreSQL with pgvector | `pgvector/pgvector:pg15` |\n| **redis** | Cache/queue | `redis:8.2` |\n\n**Setup commands:**\n```bash\ncp docker-compose.yml.example docker-compose.yml\ncp .env.template .env          # edit with your LLM provider key\ndocker compose up -d --build\n```\n\n### 2. Required Environment Variables (`.env`)\n\n**Absolutely required (server won't start without):**\n- `LLM_OPENAI_API_KEY` – LLM provider API key for OpenAI (or set `LLM_ANTHROPIC_API_KEY` / `LLM_GEMINI_API_KEY`). Honcho uses LLMs for memory extraction, summarization, dialectic chat, and dream consolidation.\n\n**Pre-configured in docker-compose (no user action needed):**\n- `DB_CONNECTION_URI=postgresql+psycopg://postgres:postgres@database:5432/postgres`\n- `CACHE_URL=redis://redis:6379/0?suppress=true`\n- `CACHE_ENABLED=true`\n\n**Key optional settings:**\n- `AUTH_USE_AUTH=false` — authentication is disabled by default\n- `VECTOR_STORE_TYPE=pgvector` — default vector store type\n- `LOG_LEVEL=INFO`\n- Deriver/Dialectic/Summary/Dream model overrides via `DERIVER_MODEL_CONFIG__MODEL`, etc.\n\n### 3. Default Port\n\n- **API server: `8000`** (bound to `127.0.0.1:8000:8000` in docker-compose)\n- Postgres: `5432`\n- Redis: `6379`\n\n### 4. Postgres Dependency\n\n**Yes, PostgreSQL with pgvector extension is required.** The docker-compose uses `pgvector/pgvector:pg15` with:\n- Database: `postgres`\n- User: `postgres`\n- Password: `postgres`\n- Init script at `database/init.sql` runs: `CREATE EXTENSION IF NOT EXISTS vector;`\n- Health check: `pg_isready -U postgres -d postgres`\n\nThe connection URI uses `postgresql+psycopg` (psycopg3 driver).\n\n### 5. Base URL Format\n\nAfter setup:\n```\nhttp://localhost:8000/v3/...\n```\n\nAPI endpoints follow the pattern `/v3/{resource}/{id}/{action}`:\n- `/v3/.../workspaces/` — create/list workspaces\n- `/v3/.../peers/` — create peers (users/agents)\n- `/v3/.../peers/{peer_id}/chat` — dialectic chat endpoint\n- `/v3/.../sessions/` — manage sessions\n- `/v3/.../messages/` — create/list messages\n- `/health` — health check endpoint\n\n### Complete Quick-Start for a Linux Server\n\n```bash\n# Clone the repo\ngit clone https://github.com/plastic-labs/honcho.git\ncd honcho\n\n# Copy config files\ncp docker-compose.yml.example docker-compose.yml\ncp .env.template .env\n\n# Edit .env — set LLM_OPENAI_API_KEY at minimum\n# nano .env\n\n# (Optional) Uncomment the development volume mounts in docker-compose.yml\n# for live reload if modifying code\n\n# Start everything\ndocker compose up -d --build\n\n# Verify it's running\ncurl http://localhost:8000/health\n```\n\n### Key Files in Repository\n\n| File | Purpose |\n|------|---------|\n| `docker-compose.yml.example` | Full Docker Compose setup |\n| `.env.template` | All environment variables with docs |\n| `Dockerfile` | Multi-stage Python 3.13 image build |\n| `docker/entrypoint.sh` | Runs DB migrations then starts FastAPI |\n| `database/init.sql` | Enables pgvector extension |\n| `config.toml.example` | TOML-based config (alternative to env vars) |\n\n### Notes for Hermes Agent Integration\n\n- **No API key needed** for Honcho itself (auth is disabled by default: `AUTH_USE_AUTH=false`)\n- You **must** provide an LLM provider API key (OpenAI, Anthropic, or Gemini) — Honcho uses it internally for memory operations\n- To use an OpenAI-compatible local LLM (Ollama, vLLM, LiteLLM), set per-module overrides like `DERIVER_MODEL_CONFIG__OVERRIDES__BASE_URL=http://host.docker.internal:11434/v1` and `DERIVER_MODEL_CONFIG__MODEL=your-model`\n- The default embedding model is `text-embedding-3-small` (OpenAI) — can also be overridden",
      "reasoning": "I've gathered all the information needed. Let me now compile a comprehensive summary of Honcho self-hosting setup.",
      "finish_reason": "stop",
      "reasoning_content": "I've gathered all the information needed. Let me now compile a comprehensive summary of Honcho self-hosting setup."
    }
  ]
}